We proposed novel score-based, online Bayesian skill learning extensions of
TrueSkill that modeled (1) player's offence and defence skills
separately, (2) how these offence and defence skills
interact to generate scores, and (3) home field advantages.  Overall these new models --- and
Gaussian-OD (using a separate offence/defence skill model) in particular ---
show an often improved ability to model winning probability and
win/loss prediction accuracy over TrueSkill, especially when the
amount of training data is limited. This indicates that there is
indeed useful information in score-based outcomes that is ignored by
TrueSkill and that separate offence/defence skill modeling does help
(c.f. the performance of Gaussian-OD vs. Gaussian-SD). The introduction of home field advantages to the models also show improved performance for some of the settings. Furthermore, these new models allow the prediction of scores (unlike TrueSkill),
with the Poisson-OD model and its fast variational Bayesian update derived
in Section~\ref{sec:PoissonInference} performing best on the
high-scoring AFL data for predicting scores. For the Poisson-OD model, we also provided a sampling based Bayesian inference approach, but results indicated that the proposed Poisson-OD variational Bayes was 1000x faster than sampling but almost always performed just as well. Altogether, these results suggested the
potential advantages of score-based Bayesian skill learning over
state-of-the-art WLD-based skill learning approaches like TrueSkill.

Future research could combine the proposed models with related work that models time-dependent skills, multi-team games, and correlated skills to utilise score-based outcomes.